CN117155748A - Modulation format identification method based on multidimensional amplitude distribution characteristics - Google Patents
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Abstract
The invention provides a modulation format identification method based on multidimensional amplitude distribution characteristics, which belongs to the technical field of communication, and comprises the following steps: acquiring signal amplitude histogram information; performing six partitioning operations on amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals according to multidimensional amplitude distribution characteristics of different modulation formats; and extracting the number of symbols in each partition of the histogram to construct a data set, and inputting the data set into a KNN (K nearest neighbor) model to identify the modulation format. The invention can realize accurate identification of 6 modulation formats (especially high-order modulation formats) in a larger OSNR range under the conditions of no optical signal to noise ratio (OSNR) priori information and lower complexity.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a modulation format identification method based on multidimensional amplitude distribution characteristics.
Background
In order to meet the increasing heterogeneous traffic demands of various data services such as internet of things, big data, cloud computing and streaming media, the concept of an Elastic Optical Network (EON) has been developed. EON can dynamically adjust the relevant parameter settings of the transceiver (e.g., modulation format, symbol rate, transmit power, etc.) according to different channel transmission conditions and quality of service requirements to maximize system transmission capacity and spectrum utilization. To guarantee the flexibility of EON, the coherent receiver in EON needs to autonomously identify the relevant parameters. Among the relevant parameters, the modulation format is one of the most important parameters. The accurate modulation format information can assist the modulation format correlation algorithm in the digital coherent receiver to realize functions such as polarization demultiplexing, frequency offset compensation, carrier phase recovery and the like, and realize demodulation of signals.
In the prior art, a data-assisted modulation format identification scheme transmits modulation format information by adding an information code or pilot frequency at a transmitting end, but can cause the reduction of spectrum efficiency or require additional operation of the transmitting end, which is possibly inapplicable in a data transmission scene with limited spectrum resources; because the high-order modulation format has a large number of clustering points in the Stokes space, the Stokes space-based identification scheme has a certain limitation in identifying the high-order modulation format; modulation format identification schemes based on Constant Mode (CMA) equalized signals do not require spatial mapping, are insensitive to frequency offset, and at the same time, CMA can compensate for residual dispersion (CD) and Polarization Mode Dispersion (PMD). However, most modulation format recognition schemes based on CMA balanced signals can only recognize 64QAM and below modulation formats, and cannot effectively recognize 128QAM; a small amount of schemes capable of identifying 128QAM require prior information such as OSNR, so that flexibility of the schemes is reduced, and complexity of the system is increased. With the development of communication technology, higher order modulation formats are more commonly applied, and therefore, a modulation format identification method is needed to accurately identify multiple modulation formats (especially higher order modulation formats) within a larger OSNR range without OSNR prior information and with lower complexity.
Disclosure of Invention
Aiming at the defects in the prior art, the modulation format identification method based on the multidimensional amplitude distribution characteristics does not need prior information such as OSNR and the like, and can realize accurate identification of various modulation formats (especially high-order modulation formats) in a larger OSNR range with lower complexity.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the scheme provides a modulation format identification method based on multidimensional amplitude distribution characteristics, which comprises the following steps:
s1, performing power normalization processing on a signal subjected to CMA equalization to obtain amplitude histogram information of the signal;
s2, according to the amplitude distribution characteristics of different modulation formats, performing six-time partitioning operation on the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals to obtain the number of symbols of the signals in each partition of the amplitude histograms, wherein the number of symbols is respectivelyN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ；
s3, partitioning the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram under different OSNR conditionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 inputting the characteristics to a KNN classifier model to obtain the KNN classifier model of the existing training set;
s4, partitioning the number of symbols in the amplitude histogram of the signal to be identifiedN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 and inputting the feature vector as a feature into a KNN classifier model of the existing training set to obtain a modulation format recognition result.
The beneficial effects of the invention are as follows: according to the distribution characteristics of the signal amplitude, the invention can accurately identify a plurality of modulation formats (especially a high-order modulation format 128 QAM) in a larger OSNR range under the condition that prior information such as symbol rate, OSNR and the like is not required to be known. Meanwhile, the scheme provided by the invention mainly extracts the effective local features, but not the global features, of the signal amplitude histogram, can effectively improve the recognition performance, has high recognition response speed, and is insensitive to frequency offset and carrier phase noise.
Further, the step S1 includes the steps of:
s101, performing power normalization processing on the signals subjected to CMA equalization;
s102, taking a plurality of symbols, and uniformly dividing a plurality of uniform amplitude intervals between the minimum value and the maximum value of the signal amplitude based on the result of power normalization processing, wherein the serial number of the first amplitude interval on the left side is 1, and the serial numbers are sequentially increased from left to right;
s103, comparing the symbol amplitude value with each amplitude interval range according to the division result, and obtaining the amplitude histogram information of the signal according to the comparison result.
The further scheme has the beneficial effects that: the invention is convenient to extract the amplitude distribution characteristics of signals with different modulation formats through the design, especially for the case of low OSNR.
Still further, the expression of the power normalization process is as follows:
wherein,representing signal numbernCorrelated probability of the amplitude of the stage +.>Representing signal numbernThe magnitude value of the stage is used to determine,mrepresenting the total number of levels of the modulation format amplitude.
The further scheme has the beneficial effects that: through the design, the signal to be identified is free from losing generality, and the signal amplitude distribution information can be conveniently and accurately obtained.
Still further, the performing six partition operations is as follows:
first partition: based on the amplitude histogram of QPSK signalsA 1 Amplitude interval toB 1 The distribution characteristic of the amplitude intervals is that the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are in the following rangeA 1 Amplitude interval toB 1 Performing a first partitioning operation on the amplitude section to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the first partitionN 1 ；
Second partition: according to the amplitude histogram of 8QAM signalsA 2 Amplitude interval toB 2 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 2 Amplitude interval toB 2 Performing a second partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the second partitionN 2 ；
Third partition: according to 16QAM signalsA 3 Amplitude interval toB 3 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 3 Amplitude interval toB 3 Performing a third partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the third partitionN 3 ；
Fourth partition: according to 32QAM signalsA 4 Amplitude interval toB 4 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 4 Amplitude interval toB 4 Performing fourth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fourth partitioningN 4 ；
Fifth partition: according to 64QAM signalsA 5 Amplitude interval toB 5 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 5 Amplitude interval toB 5 Performing fifth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fifth partitionN 5 ；
Sixth partition: according to 128QAM signalsA 6 Amplitude interval toB 6 Amplitude distribution characteristic features, the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are set in the following rangeA 6 Amplitude interval toB 6 Performing a sixth partitioning operation on the amplitude interval to obtain QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the sixth partitioningNumber of symbols in (a)N 6 。
The beneficial effects of the above-mentioned further scheme are: the present invention effectively distinguishes local features of amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM, and 128QAM signals according to performing six partitioning operations.
Still further, the number of symbolsThe expression of (2) is as follows:
wherein,after the first partition is representedA 1 Amplitude interval toB 1 The number of symbols of the amplitude interval,A 1 a sequence number representing the amplitude interval of the starting position in the first sub-zone,B 1 sequence number indicating the amplitude interval of the end position in the first sub-zone,/->Represent the firstxThe number of symbols for each amplitude interval;
the number of symbolsThe expression of (2) is as follows:
wherein,N 2 representing after the second partitionA 2 Amplitude interval toB 2 The number of symbols of the amplitude interval,A 2 a sequence number representing the amplitude interval of the starting position in the second partition,B 2 a sequence number indicating an end position amplitude interval in the second partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 3 representing after the third partitionA 3 Amplitude interval toB 3 The number of symbols of the amplitude interval,A 3 a sequence number representing the amplitude interval of the start position in the third partition,B 3 a sequence number indicating an amplitude interval of the end position in the third partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 4 after the fourth partition is representedA 4 Amplitude interval toB 4 The number of symbols of the amplitude interval,A 4 a sequence number representing the amplitude interval of the start position in the fourth sub-zone,B 4 a sequence number representing an amplitude interval of an end position in the fourth sub-section;
the number of symbolsThe expression of (2) is as follows:
wherein,N 5 representing after the fifth partitioningA 5 Amplitude interval toB 5 The number of symbols of the amplitude interval,A 5 a sequence number representing the amplitude interval of the starting position in the fifth partition,B 5 a sequence number representing an amplitude interval of an end position in the fifth partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 6 representing after the sixth partitionA 6 Amplitude interval toB 6 The number of symbols of the amplitude interval,A 6 a sequence number representing the amplitude interval of the starting position in the sixth partition,B 6 and the sequence number of the amplitude interval of the ending position in the sixth partition.
The further scheme has the beneficial effects that: according to the operation, the local effective characteristic information of the amplitude histograms of different modulation formats is extracted, the number of symbols in each partition is converted into one-dimensional data, six-dimensional data are built by the number of symbols in six partitions, so that the effectiveness and the distinguishing degree of the characteristics are improved, the complexity of the overall characteristic identification based on the histograms is reduced, and the data set is built for training and testing a KNN classifier model.
Still further, the step S3 specifically includes:
acquiring the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram partitions for different OSNR casesN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ：
for each modulation format, a training sample is constructed by the symbol number distribution of a plurality of CMA equalized symbols in different amplitude histogram partitions, wherein each group of training samples is seven-dimensional data, and the former six dimensions are the corresponding symbol numbers in different amplitude histogram partitionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 the seventh dimension is a label;
and inputting the training sample into the KNN classifier model to obtain the KNN classifier model of the existing training set.
The further scheme has the beneficial effects that: for multidimensional data, simple threshold decision cannot be passed; in the KNN classifier model of the existing training set, the problem of reduced recognition performance under the condition of low OSNR can be effectively relieved through joint judgment of multi-dimensional data in a multi-dimensional data space. The training set is constructed by adopting multidimensional amplitude distribution characteristics of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals under different OSNR conditions, so that the KNN model of the existing training set can be ensured to realize accurate identification of six modulation formats in a larger OSNR range.
Still further, the step S4 specifically includes:
for the signal to be identified, distributing the symbols after the equalization of a plurality of CMAs in different partitions of the amplitude histogramN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 constructed as a test sample;
and inputting the test sample into a KNN classifier model of the existing training set for recognition, and obtaining a modulation format recognition result.
The further scheme has the beneficial effects that: based on the KNN classifier model of the existing training set, the recognition accuracy is effectively improved through joint judgment of multi-dimensional data in a multi-dimensional data space, and accurate recognition of six modulation formats can be realized in a large OSNR range.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a simulation setup diagram of a coherent optical communication system according to an embodiment of the present invention.
Fig. 3 is a block diagram of the digital signal processing module of fig. 2.
Fig. 4 is a block diagram of the amplitude histogram of the QPSK signal.
Fig. 5 is a schematic diagram of the partitioning operation of an 8QAM signal amplitude histogram.
Fig. 6 is a schematic diagram of the partitioning operation of the 16QAM signal amplitude histogram.
Fig. 7 is a block diagram of a 32QAM signal amplitude histogram.
Fig. 8 is a block diagram of a 64QAM signal amplitude histogram.
Fig. 9 is a block diagram of a 128QAM signal amplitude histogram.
Fig. 10 is a process diagram of modulation format recognition by the KNN classifier model.
Fig. 11 is a graph showing the variation of the correct recognition rate of the modulation format with OSNR according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
Examples
As shown in fig. 1, the method for identifying the modulation format based on the multidimensional amplitude distribution features comprises the following steps:
s1, performing power normalization processing on the signals subjected to CMA equalization to obtain amplitude histogram information of the signals, wherein the implementation method comprises the following steps:
s101, performing power normalization processing on the signals subjected to CMA equalization;
s102, taking a plurality of symbols, and uniformly dividing a plurality of uniform amplitude intervals between the minimum value and the maximum value of the signal amplitude based on the result of power normalization processing, wherein the serial number of the first amplitude interval on the left side is 1, and the serial numbers are sequentially increased from left to right;
s103, comparing the symbol amplitude value with each amplitude interval range according to the division result, and obtaining the amplitude histogram information of the signal according to the comparison result.
In this embodiment, in order to not lose generality, the signal after CMA equalization is first subjected to power normalization, where the power normalization formula is as follows:
wherein,representing signal numbernCorrelated probability of the amplitude of the stage +.>Representing signal numbernThe magnitude value of the stage is used to determine,mrepresenting the total number of levels of the modulation format amplitude.
S2, according to the amplitude distribution characteristics of different modulation formats, performing six-time partitioning operation on the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals to obtain the number of symbols of the signals in each partition of the amplitude histograms, wherein the number of symbols is respectivelyN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ；
in this embodiment, six partitioning operations are performed on the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM signals:
first partition: based on the amplitude histogram of QPSK signalsA 1 Amplitude interval toB 1 The distribution characteristic of the amplitude interval is different from other modulation formats, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the following modesA 1 Amplitude interval toB 1 Performing a first partitioning operation on the amplitude section to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the first partitionN 1 The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 4, the ordinate in the figure represents symbol data, the abscissa represents the bin number of the histogram, and the OSNR of the QPSK signal in the figure is 26dB.
Second partition: according to the amplitude histogram of 8QAM signalsA 2 Amplitude interval toB 2 The amplitude interval has kurtosis distribution characteristic, and is different from other modulation formats, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 2 Amplitude interval toB 2 Performing a second partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the second partitionN 2 The method comprises the steps of carrying out a first treatment on the surface of the As shown in FIG. 5, in whichThe ordinate represents symbol data, the abscissa represents the bin number of the histogram, and OSNR of the 8QAM signal in the figure is 31dB.
Third partition: according to 16QAM signalsA 3 Amplitude interval toB 3 The amplitude interval has kurtosis distribution characteristic, and is different from other modulation formats, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 3 Amplitude interval toB 3 Performing a third partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the third partitionN 3 The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 6, the ordinate in the figure represents symbol data, the abscissa represents the bin number of the histogram, and the OSNR of the 16QAM signal in the figure is 33dB.
Fourth partition: according to 32QAM signalsA 4 Amplitude interval toB 4 The amplitude interval has kurtosis distribution characteristic, and is different from other modulation formats, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 4 Amplitude interval toB 4 Performing fourth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fourth partitioningN 4 The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 7, the ordinate in the figure represents symbol data, the abscissa represents the bin number of the histogram, and the OSNR of the 32QAM signal in the figure is 36dB.
Fifth partition: according to 64QAM signalsA 5 Amplitude interval toB 5 The amplitude interval has kurtosis distribution characteristic, and is different from other modulation formats, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 5 Amplitude interval toB 5 Performing fifth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fifth partitionN 5 The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 8, the ordinate in the figure represents symbol data, the abscissa represents the bin number of the histogram, and the OSNR of the 64QAM signal in the figure is 38dB.
Sixth partition: according to 128QAM signalsA 6 Amplitude interval toB 6 Amplitude interval distribution characteristicsCharacteristic of the signal is different from other modulation formats, and amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are obtainedA 6 Amplitude interval toB 6 Performing a sixth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the sixth partitionN 6 The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 9, the ordinate in the figure represents symbol data, the abscissa represents the bin number of the histogram, and the OSNR of the 128QAM signal in the figure is 44dB.
In the present embodiment, the number of symbols of each partitionN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 the expressions of (2) are as follows:
the number of symbolsThe expression of (2) is as follows:
wherein,after the first partition is representedA 1 Amplitude interval toB 1 The number of symbols of the amplitude interval,A 1 a sequence number representing the amplitude interval of the starting position in the first sub-zone,B 1 sequence number indicating the amplitude interval of the end position in the first sub-zone,/->Represent the firstxThe number of symbols for each amplitude interval;
the number of symbolsThe expression of (2) is as follows:
wherein,N 2 representing after the second partitionA 2 Amplitude interval toB 2 The number of symbols of the amplitude interval,A 2 a sequence number representing the amplitude interval of the starting position in the second partition,B 2 a sequence number indicating an end position amplitude interval in the second partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 3 representing after the third partitionA 3 Amplitude interval toB 3 The number of symbols of the amplitude interval,A 3 a sequence number representing the amplitude interval of the start position in the third partition,B 3 a sequence number indicating an amplitude interval of the end position in the third partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 4 after the fourth partition is representedA 4 Amplitude interval toB 4 The number of symbols of the amplitude interval,A 4 a sequence number representing the amplitude interval of the start position in the fourth sub-zone,B 4 a sequence number representing an amplitude interval of an end position in the fourth sub-section;
the number of symbolsThe expression of (2) is as follows:
wherein,N 5 representing after the fifth partitioningA 5 Amplitude interval toB 5 The number of symbols of the amplitude interval,A 5 a sequence number representing the amplitude interval of the starting position in the fifth partition,B 5 a sequence number representing an amplitude interval of an end position in the fifth partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 6 representing after the sixth partitionA 6 Amplitude interval toB 6 The number of symbols of the amplitude interval,A 6 a sequence number representing the amplitude interval of the starting position in the sixth partition,B 6 and the sequence number of the amplitude interval of the ending position in the sixth partition.
S3, partitioning the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram under different OSNR conditionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 the characteristics are input into a KNN classifier model to obtain the KNN classifier model of the existing training set, and the KNN classifier model specifically comprises the following components:
acquiring the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram partitions for different OSNR casesN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ：
for each modulation format, a training sample is constructed by the symbol number distribution of a plurality of CMA equalized symbols in different amplitude histogram partitions, wherein each group of training samples is seven-dimensional data, and the former six dimensions are the corresponding symbol numbers in different amplitude histogram partitionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 the seventh dimension is a label;
and inputting the training sample into the KNN classifier model to obtain the KNN classifier model of the existing training set.
In this embodiment, to obtain the KNN classifier model of the existing training set, the number of symbols in each partition of the amplitude histogram of the QPSK, 8QAM, 16QAM, 32QAM, 64QAM, and 128QAM signals under different OSNR conditions is obtainedN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 each modulation format is formed by distributing the number of symbols of 8000 CMA balanced symbols in different amplitude histogram partitions, 20 different OSNR values (QPSK: 7-26dB, 8QAM:12-31dB, 16QAM:14-33dB, 32QAM:18-37dB, 64QAM:19-38dB, 128QAM: 25-44dB and 1dB interval) are selected for each modulation format, and each OSNR value comprises 80 groups of training samples, namely 1600 groups of training samples. Each group of training samples is seven-dimensional data, and the first six dimensions are the number of corresponding symbols in different partitionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 dimension 7 is the label, i.e., the modulation format class to which the training samples correspond. A total of 9600 sets of training samples were input to the KNN classifier model, resulting in a KNN classifier model of the existing training set.
S4, partitioning the number of symbols in the amplitude histogram of the signal to be identifiedN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 and inputting the feature vector as a feature into a KNN classifier model of the existing training set for recognition, and obtaining a modulation format recognition result. The method comprises the following steps:
for the signal to be identified, distributing the symbols after the equalization of a plurality of CMAs in different partitions of the amplitude histogramN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 constructed as a test sample;
and inputting the test sample into a KNN classifier model of the existing training set for recognition, and obtaining a modulation format recognition result.
In this embodiment, to obtain a detailed test result, 20 sets of test samples are selected for each OSNR value of different modulation formats, 400 sets of test samples are selected for each modulation format, a set of test samples is randomly selected from 2400 sets of test samples, and is put into a KNN classifier model of an existing training set for recognition, so as to obtain a modulation format class corresponding to the set of data. As shown in fig. 10, the test sample of the modulation format to be identified includesN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 six-dimensional data is obtained by inputting test samples into KNN classifier model of existing training set, triangle in FIG. 10 represents test sample, KNN classifier model calculates test sample and training sample (diamond in FIG. 10 represents training sample type A, star represents training sample type B, X) 1 , X 2 , X 3 ,... ,X n Representing the calculated distance between the test sample and the training sample, X n Representing the distance calculated by the test sample from the nth training sample). For example, in fig. 10, when k=3 (K represents K training samples closest to the test sample in the KNN classifier model), three training samples closest to the test sample (i.e., comparison distances) are calculated, respectively, one diamond training sample with a class and two star training samples with B class (i.e., training samples) in fig. 10. Finally, judging that two training samples in three training samples closest to the test sample are of type B; and judging the test sample as the training sample category with the largest number of the 3 training samples (namely classifying the test sample as the category with the largest number), namely judging that the triangle test sample belongs to the type B, and obtaining a prediction result. According to the size of the training sample, a proper K value needs to be selected, and excessive or insufficient K value can cause the reduction of the recognition performance of the classifier model, so that the optimal K value is selected, and then the distance between the test sample and the training sample is calculated to obtain the KNN classifier model with better recognition performance.
To verify the feasibility of the proposal of the inventionThe simulation block diagram of the 28GBaud polarization multiplexing (PDM) -QPSK/-8QAM/-16QAM/-32QAM/-64QAM/-128QAM signal transmission is simulated numerically, a simulation block diagram of the 28GBaud PDM coherent optical communication system is shown in figure 2, the simulation block diagram comprises a transmitting end and a receiving end, at the transmitting end, the wavelength of continuous waves emitted by a laser is 1550nm, the line width is 100kHz, an electric signal drives an IQ modulator to generate QPSK/8QAM/16 QAM/32 QAM/64QAM/128QAM signals with the transmission rate of 28GBaud, and the QPSK/8QAM/16 QAM/64QAM/128QAM signals are converted into the polarization multiplexing signals through a polarization beam combiner to enter a transmission channel. The OSNR setting module can adjust OSNR parameters, and in the simulation, the transmission channels under different OSNR conditions can be simulated by adjusting the OSNR setting module parameters. At the receiving end, the out-of-band noise of the optical signal is filtered by an optical band-pass filter, then the optical signal is divided into two paths by a polarization beam splitter, and the two paths of local oscillation optical signals respectively separated by a local oscillation laser are utilized to 90 0 Optical mixer 90 0 Mixing, converting the mixed signals into electric signals through a balance light detector, filtering the electric signals based on a low-pass filter, digitizing the electric signals based on an analog-to-digital converter, and then carrying out digital signal processing on the analog-to-digital converted electric signals.
The analog-to-digital converted signal enters a digital signal processing module, as shown in fig. 3. The whole digital signal processing module comprises three parts: modulation format uncorrelated algorithm, modulation format recognition scheme and modulation format correlated algorithm proposed by the invention. First, digital signals are processed using algorithms that are not related to the modulation format, such as dispersion compensation algorithms, clock recovery algorithms, and constant modulus equalization algorithms. The dispersion compensation algorithm and the clock recovery algorithm compensate for dispersion and clock jitter. The constant modulus equalization algorithm can be realizedmPolarization demultiplexing of PSK (multi-system phase modulation) signals while compensating for residual dispersion and polarization mode dispersion. However, for higher orders [ ]m>4）mQAM (quadrature amplitude modulation) signals can only realize preliminary polarization demultiplexing, and specific polarization demultiplexing is also required after the modulation format is determined. The invention provides that the modulation format information of the signal is known in consideration of the following polarization demultiplexing and other modulation format related algorithmsThe modulation format identification scheme precedes the modulation format correlation algorithm (i.e., the multi-stage modulo-length algorithm, the frequency offset compensation algorithm, the carrier phase recovery algorithm, and the symbol decisions) to provide modulation format information.
The correct recognition rate of the modulation format recognition method provided by the invention under different OSNR conditions is shown in figure 11, in the figure, the ordinate is the correct recognition rate (%), the abscissa is the optical signal-to-noise ratio (dB), the OSNR ranges of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are respectively 7-26dB, 12-31dB, 14-33dB, 18-37dB, 19-38dB and 25-44dB, and the OSNR interval is 1dB. For 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64 QAM/-128QAM signals, the minimum OSNR required to achieve 100% correct recognition Rate is 7dB, 12dB, 15dB, 19dB, 21dB and 25dB, respectively, each below the corresponding OSNR threshold (Bit Error Rate (BER) =2.4X10) for 20% forward Error correction (Forward Error Correction, FEC) respectively -2 Corresponding to the vertical dashed line in fig. 11).
Claims (7)
1. The modulation format identification method based on the multidimensional amplitude distribution characteristics is characterized by comprising the following steps of:
s1, performing power normalization processing on a signal subjected to CMA equalization to obtain amplitude histogram information of the signal;
s2, according to the amplitude distribution characteristics of different modulation formats, performing six-time partitioning operation on the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals to obtain the number of symbols of the signals in each partition of the amplitude histograms, wherein the number of symbols is respectivelyN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ；
s3, partitioning the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram under different OSNR conditionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 inputting the characteristics to a KNN classifier model to obtain the KNN classifier model of the existing training set;
S4、partitioning the number of symbols in an amplitude histogram of a signal to be identifiedN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 and inputting the feature vector as a feature into a KNN classifier model of the existing training set for recognition, and obtaining a modulation format recognition result.
2. The modulation format recognition method based on the multi-dimensional amplitude distribution feature according to claim 1, wherein S1 comprises the steps of:
s101, performing power normalization processing on the signals subjected to CMA equalization;
s102, taking a plurality of symbols, and uniformly dividing a plurality of uniform amplitude intervals between the minimum value and the maximum value of the signal amplitude based on the result of power normalization processing, wherein the serial number of the first amplitude interval on the left side is 1, and the serial numbers are sequentially increased from left to right;
s103, comparing the symbol amplitude value with each amplitude interval range according to the division result, and obtaining the amplitude histogram information of the signal according to the comparison result.
3. The modulation format recognition method based on the multidimensional amplitude distribution feature according to claim 2, wherein the expression of the power normalization processing is as follows:
wherein,representing signal numbernCorrelated probability of the amplitude of the stage +.>Representing signal numbernThe magnitude value of the stage is used to determine,mrepresenting the total number of levels of the modulation format amplitude.
4. The modulation format recognition method based on the multi-dimensional amplitude distribution feature according to claim 1, wherein the performing six partitioning operations is as follows:
first partition: based on the amplitude histogram of QPSK signalsA 1 Amplitude interval toB 1 The distribution characteristic of the amplitude intervals is that the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are in the following rangeA 1 Amplitude interval toB 1 Performing a first partitioning operation on the amplitude section to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the first partitionN 1 ；
Second partition: according to the amplitude histogram of 8QAM signalsA 2 Amplitude interval toB 2 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 2 Amplitude interval toB 2 Performing a second partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the second partitionN 2 ；
Third partition: according to 16QAM signalsA 3 Amplitude interval toB 3 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 3 Amplitude interval toB 3 Performing a third partitioning operation on the amplitude interval to obtain the symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the third partitionN 3 ；
Fourth partition: according to 32QAM signalsA 4 Amplitude interval toB 4 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 4 Amplitude interval toB 4 Performing fourth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fourth partitioningN 4 ；
Fifth partition: according to 64QAM signalsA 5 Amplitude interval toB 5 The amplitude interval has kurtosis distribution characteristic, and the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are distributed in the amplitude histogramA 5 Amplitude interval toB 5 Performing fifth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the fifth partitionN 5 ；
Sixth partition: according to 128QAM signalsA 6 Amplitude interval toB 6 Amplitude distribution characteristic features, the amplitude histograms of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals are set in the following rangeA 6 Amplitude interval toB 6 Performing a sixth partitioning operation on the amplitude interval to obtain symbol numbers of QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals in the sixth partitionN 6 。
5. The modulation format recognition method based on the multi-dimensional amplitude distribution feature according to claim 1, wherein the number of symbolsThe expression of (2) is as follows:
wherein,after the first partition is representedA 1 Amplitude interval toB 1 The number of symbols of the amplitude interval,A 1 a sequence number representing the amplitude interval of the starting position in the first sub-zone,B 1 sequence number indicating the amplitude interval of the end position in the first sub-zone,/->Represent the firstxThe number of symbols for each amplitude interval;
the number of symbolsThe expression of (2) is as follows:
wherein,N 2 representing after the second partitionA 2 Amplitude interval toB 2 The number of symbols of the amplitude interval,A 2 a sequence number representing the amplitude interval of the starting position in the second partition,B 2 a sequence number indicating an end position amplitude interval in the second partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 3 representing after the third partitionA 3 Amplitude interval toB 3 The number of symbols of the amplitude interval,A 3 a sequence number representing the amplitude interval of the start position in the third partition,B 3 a sequence number indicating an amplitude interval of the end position in the third partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 4 after the fourth partition is representedA 4 Amplitude interval toB 4 The number of symbols of the amplitude interval,A 4 a sequence number representing the amplitude interval of the start position in the fourth sub-zone,B 4 a sequence number representing an amplitude interval of an end position in the fourth sub-section;
the number of symbolsThe expression of (2) is as follows:
wherein,N 5 representing after the fifth partitioningA 5 Amplitude interval toB 5 The number of symbols of the amplitude interval,A 5 a sequence number representing the amplitude interval of the starting position in the fifth partition,B 5 a sequence number representing an amplitude interval of an end position in the fifth partition;
the number of symbolsThe expression of (2) is as follows:
wherein,N 6 representing after the sixth partitionA 6 Amplitude interval toB 6 The number of symbols of the amplitude interval,A 6 a sequence number representing the amplitude interval of the starting position in the sixth partition,B 6 and the sequence number of the amplitude interval of the ending position in the sixth partition.
6. The modulation format recognition method based on the multidimensional amplitude distribution feature according to claim 1, wherein S3 specifically is:
acquiring the number of symbols in QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signal amplitude histogram partitions for different OSNR casesN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 ：
for each modulation format, a training sample is constructed by the symbol number distribution of a plurality of CMA equalized symbols in different amplitude histogram partitions, wherein each group of training samples is seven-dimensional data, and the former six dimensions are the corresponding symbol numbers in different amplitude histogram partitionsN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 the seventh dimension is a label;
and inputting the training sample into the KNN classifier model to obtain the KNN classifier model of the existing training set.
7. The modulation format recognition method based on the multidimensional amplitude distribution feature according to claim 1, wherein S4 specifically is:
for the signal to be identified, distributing the symbols after the equalization of a plurality of CMAs in different partitions of the amplitude histogramN 1 、N 2 、N 3 、N 4 、N 5 AndN 6 constructed as a test sample;
and inputting the test sample into a KNN classifier model of the existing training set for recognition, and obtaining a modulation format recognition result.
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